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NMT

Neural machine translation is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model.

Papers

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Showing 526–550 of 1773 papers

TitleStatusHype
Digging Errors in NMT: Evaluating and Understanding Model Errors from Hypothesis Distribution—0
Contrastive Word Embedding Learning for Neural Machine Translation—0
Modeling Multi-granularity Segmentation for Rare Words in Neural Machine Translation—0
Towards Full Utilization on Mask Task for Distilling PLMs into NMT—0
Translation Transformers Rediscover Inherent Data DomainsCode0
Scaling Laws for Neural Machine Translation—0
Efficient Inference for Multilingual Neural Machine Translation—0
Non-Parametric Unsupervised Domain Adaptation for Neural Machine TranslationCode1
Fine Grained Human Evaluation for English-to-Chinese Machine Translation: A Case Study on Scientific Text—0
Contrastive Learning for Context-aware Neural Machine TranslationUsing Coreference Information—0
Multi-Sentence Resampling: A Simple Approach to Alleviate Dataset Length Bias and Beam-Search DegradationCode0
Evaluating Multiway Multilingual NMT in the Turkic LanguagesCode1
Attention Weights in Transformer NMT Fail Aligning Words Between Sequences but Largely Explain Model Predictions—0
Modeling Concentrated Cross-Attention for Neural Machine Translation with Gaussian Mixture Model—0
Rethinking Zero-shot Neural Machine Translation: From a Perspective of Latent VariablesCode1
Rule-based Morphological Inflection Improves Neural Terminology TranslationCode0
Neural Machine Translation Quality and Post-Editing PerformanceCode0
Improving Multilingual Translation by Representation and Gradient RegularizationCode1
BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine TranslationCode1
A Large-Scale Study of Machine Translation in the Turkic Languages—0
Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data SelectionCode0
HintedBT: Augmenting Back-Translation with Quality and Transliteration Hints—0
Fixing exposure bias with imitation learning needs powerful oracles—0
Vision Matters When It Should: Sanity Checking Multimodal Machine Translation ModelsCode0
Attention based Sequence to Sequence Learning for Machine Translation of Low Resourced Indic Languages -- A case of Sanskrit to Hindi—0
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